Di kalangan mahasiswa teknik ataupun komputer, image processing adalah sesuatu yang paling menarik. Karena dengan mendalaminya akan ada banyak yang bisa kita lakukan dalam kehidupan sehari-hari, bahkan tidak hanya itu yang paling penting bagi mahasiswa adalah adanya topik yang bisa dikejarkan untuk syarat kelulusan S1 / D3, yess its skripsi. Ada banyak topik yang bisa diambil dari image processing, biasanya seperti image recognition, image segmentation, analisis algoritma image processing, dsb. Show Pada kesempatan kali ini, admin akan mencoba untuk memberikan episode khusus untuk mendalami image processing menggunakan python dan opencv. So, stay tune with this blog. Belajar image processing - integrasi library opencv - python dan open image Ada tiga syarat agar image processing bisa dilakukan menggunakan python: 1. Python 2 (bukan 3) 2. Library Open CV 3. Library Numpy Well, ikuti step berikut. 1. Install python2 apabila belum liat tutorial ini. 2. Download opencv for windows pada website www.opencv.org. 3. Install / extrace opencv, setelah itu buka foldernya pada alamat opencv/build/python2.7 4. Copy cv2.pyd pada c:/python27/lib/site-packages 5. Setelah itu coba buka python dan ketik import cv2, apabila sukses well done! Import library opencv - apabila sukses then great!6. Setelah itu install numpy dan matplotlib menggunakan pip 7. Well done! Test dulu biar lebih menarik. Buat folder baru di C:/Python2/ dengan nama folder ImageProcessing kemudian buka CMD dan ketik cd C:/Python2/ImageProcessing/ 8. Copy image yang mau kamu lakukan image processing pada folder ini, kemudian buka python idle dan ketik script dibawah. (sesuaikan nama image dengan punyamu). Tested in Ubuntu + Intel i7 CPU + Nvidia Titan X (Pascal) with Cuda (>=8.0) and CuDNN (>=5.0). CPU mode should also work with minor changes. Quick Start (Testing)
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DataIf you want to experiment on the data in our evaluation, please email to [email protected]. CitationIf you use our code for research, please cite our paper: Qifeng Chen, Jia Xu, and Vladlen Koltun. Fast Image Processing with Fully-Convolutional Networks. In ICCV 2017. This tutorial demonstrates using Visual Studio Code and the Microsoft Python extension with common data science libraries to explore a basic data science scenario. Specifically, using passenger data from the Titanic, you will learn how to set up a data science environment, import and clean data, create a machine learning model for predicting survival on the Titanic, and evaluate the accuracy of the generated model. PrerequisitesThe following installations are required for the completion of this tutorial. Make sure to install them if you haven't already.
Set up a data science environmentVisual Studio Code and the Python extension provide a great editor for data science scenarios. With native support for Jupyter notebooks combined with Anaconda, it's easy to get started. In this section, you will create a workspace for the tutorial, create an Anaconda environment with the data science modules needed for the tutorial, and create a Jupyter notebook that you'll use for creating a machine learning model.
Prepare the dataThis tutorial uses the Titanic dataset available on OpenML.org, which is obtained from Vanderbilt University's Department of Biostatistics at https://hbiostat.org/data. The Titanic data provides information about the survival of passengers on the Titanic and characteristics about the passengers such as age and ticket class. Using this data, the tutorial will establish a model for predicting whether a given passenger would have survived the sinking of the Titanic. This section shows how to load and manipulate data in your Jupyter notebook.
Train and evaluate a modelWith the dataset ready, you can now begin creating a model. For this section, you'll use the scikit-learn library (as it offers some useful helper functions) to do pre-processing of the dataset, train a classification model to determine survivability on the Titanic, and then use that model with test data to determine its accuracy.
(Optional) Use a neural networkA neural network is a model that uses weights and activation functions, modeling aspects of human neurons, to determine an outcome based on provided inputs. Unlike the machine learning algorithm you looked at previously, neural networks are a form of deep learning wherein you don't need to know an ideal algorithm for your problem set ahead of time. It can be used for many different scenarios and classification is one of them. For this section, you'll use the Keras library with TensorFlow to construct the neural network, and explore how it handles the Titanic dataset.
Next stepsNow that you're familiar with the basics of performing machine learning within Visual Studio Code, here are some other Microsoft resources and tutorials to check out. |